Abstract： Arsenic and arsenic compounds have been listed as one of the toxic and harmful environment pollutants, and drinking, seafood intake, use of skincare products and inhalation of tobacco smoke are main routes of exposure to human arsenic exposure. The adverse effects of arsenic on pregnant outcomes have been paid much attention. Prenatal exposure to high-level arsenic has been found to increase the risk of spontaneous abortion among pregnant women. Based on national and international epidemiological studies on the correlation between arsenic exposure and spontaneous abortion during the period between 1992 and 2020, we review the association between arsenic exposure and spontaneous abortion and describe the mechanisms underlying spontaneous abortion caused by arsenic exposure, so as to provide insights into early prevention of spontaneous abortion.

Objective:To establish a long-term mortality rate prediction model for patients aged 60 years and over with atrial fibrillation and coronary heart disease using the machine learning method, and identify the corresponding risk factors of mortality.Methods:In this retrospective cohort study, a total of 329(11 cases lost of follow-up)patients with 183 males(55.6%)and 146 females(44.4%), aged(77.8±7.3)years, and 142 patients aged 80 years or older(43.2%)were selected in our hospitals from January 2013 to March 2015.And their clinical data on atrial fibrillation and coronary heart disease were analyzed.They were divided into the death group(151 cases)and the survival group(167 cases)according to the survival outcome.In addition, 60 patients aged 60 years and over admitted to our hospitals from April to July 2015 with atrial fibrillation and coronary heart disease were selected as external data validation set.The clinical data included age, gender, body mass index, diagnosis, co-morbidity, laboratory indicators, electrocardiogram, echocardiogram, treatment data.These patients were followed up for at least 6 years, and the main adverse cardiovascular and cerebrovascular events(MACCE), including death, were recorded.Finally, the data of the enrolled patients were randomly divided into the training set and the test set according to the ratio of 9∶1, Different models were established to predict the long-term mortality of patients with atrial fibrillation and coronary heart disease by machine learning algorithm.The optimal model was established by substituting external data(60 cases)into the model for verification and comparison.The top 20 risk factors for mortality were determined by Shapley additive explanation(SHAP)algorithm.Results:A total of 329 hospitalized patients were included in this study, the overall median follow-up time was 77.0 months(95% CI: 54.0~84.0), 11 cases lost during follow-up(3.3%), and 151 cases died(45.9%). The analysis found that the areas under the ROC curve for a support vector machine(SVM)model, k-Nearest Neighbor(KNN)model, decision tree model, random forest model, ADABoost model, XGBoost model and logistic regression model were 0.76, 0.75, 0.75, 0.91, 0.86, 0.85 and 0.81, respectively.The random forest model had the highest prediction efficiency, with the accuracy of 0.789 and F1 value of 0.806, which was better than the logistic regression model[the Area Under Receiver Operating Characteristic Curve(AUC): 0.91 vs.0.81, P<0.05]. D-dimer, age, number of MACCE, left ventricular ejection fraction, serum albumin level, anemia, New York Heart Association(NYHA)grade, history of old myocardial infarction, estimated glomerular filtration rate(eGFR)and resting heart rate were important risk factors for predicting long-term mortality. Conclusions:The random forest model based on machine learning method can predict the long-term mortality of patients with atrial fibrillation and coronary heart disease aged 60 years and over, have a good identification ability.Its accuracy is higher than that of the traditional Logistic regression model.Reducing the long-term mortality and improving the long-term outcomes can be achieved by intervening on D-dimer levels, correcting hypoproteinemia and anemia, improving cardiac function and controlling resting ventricular rates.

Objective To investigate the growth, development and health status of primary and secondary school students in Xinzhou District of Wuhan, and analyze the detection and change trend of common diseases in primary and secondary school students, and to provide a basis for relevant departments to formulate prevention and control measures of common diseases in students. Methods The monitoring data of common diseases and health influencing factors of primary and secondary school students in Xinzhou District from 2019 to 2022 were analyzed and compared according to different genders, different grades and ages. SPSS 20.0 software was used to analyze the data of detection rates of myopia, dental caries, obesity, malnutrition and abnormal spinal curvature. Results The overall detection rates of myopia, dental caries, malnutrition, obesity and abnormal spinal curvature were 57.00%, 58.45%, 4.60%, 14.91%, and 6.33%, respectively, in Xinzhou District from 2019 to 2022. The annual change rates were 7.22%, 15.10%, -2.72%, 13.29%, and 4.91%, respectively. The detection rates of myopia, dental caries, obesity and abnormal spinal curvature showed an increasing trend in each year (χ² ≥17.22, P<0.001). The detection rates of myopia and malnutrition increased with the increase of age and school level (both χ²≥42.37, P<0.001), while the opposite was true for the detection rates of dental caries and obesity (both χ²≥14.26, P<0.001). The detection rates of myopia and dental caries were higher in girls than in boys (both χ²≥33.66, P<0.001), while the detection rates of obesity and abnormal spinal curvature were higher in boys than in girls (both χ²≥8.22, P<0.005). The detection rates of myopia, dental caries, obesity and abnormal spinal curvature in 2019 were lower than those in 2020-2022 (χ²≥4.11, P<0.05), while the detection rates of malnutrition had decreased. Conclusion The growth, development and health status of primary and secondary school students in Xinzhou District are serious. The detection rate of common diseases such as myopia, dental caries, obesity and abnormal curvature of the spine is on the rise, which should be the focus of the surveillance work of common diseases in primary and secondary school students in the future, and comprehensive intervention measures are urgently needed to prevent and control these common diseases.